Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory243.6 MiB
Average record size in memory197.0 B

Variable types

Numeric17
Text6
Categorical4
Boolean13

Alerts

amt is highly overall correlated with amt_day_interaction and 2 other fieldsHigh correlation
amt_day_interaction is highly overall correlated with amt and 2 other fieldsHigh correlation
amt_merchant_interaction is highly overall correlated with amt and 2 other fieldsHigh correlation
amt_ratio is highly overall correlated with amt and 2 other fieldsHigh correlation
day is highly overall correlated with day_of_monthHigh correlation
day_of_month is highly overall correlated with dayHigh correlation
day_of_week is highly overall correlated with amt_day_interaction and 1 other fieldsHigh correlation
is_weekend is highly overall correlated with day_of_weekHigh correlation
merchant_encoded is highly overall correlated with amt_merchant_interactionHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
high_value is highly imbalanced (71.4%)Imbalance
category_food_dining is highly imbalanced (63.2%)Imbalance
category_gas_transport is highly imbalanced (52.6%)Imbalance
category_grocery_net is highly imbalanced (78.1%)Imbalance
category_grocery_pos is highly imbalanced (54.6%)Imbalance
category_health_fitness is highly imbalanced (64.8%)Imbalance
category_home is highly imbalanced (54.7%)Imbalance
category_kids_pets is highly imbalanced (57.3%)Imbalance
category_misc_net is highly imbalanced (71.9%)Imbalance
category_misc_pos is highly imbalanced (66.7%)Imbalance
category_personal_care is highly imbalanced (63.4%)Imbalance
category_shopping_net is highly imbalanced (61.5%)Imbalance
category_shopping_pos is highly imbalanced (56.4%)Imbalance
category_travel is highly imbalanced (79.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
amt_ratio is highly skewed (γ1 = 34.80955403)Skewed
amt_merchant_interaction is highly skewed (γ1 = 50.34670773)Skewed
amt_day_interaction is highly skewed (γ1 = 62.28714456)Skewed
day_of_week has 254282 (19.6%) zerosZeros
hour has 42502 (3.3%) zerosZeros
amt_day_interaction has 254282 (19.6%) zerosZeros

Reproduction

Analysis started2024-09-15 08:45:47.343690
Analysis finished2024-09-15 08:49:10.288925
Duration3 minutes and 22.95 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

amt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:10.440564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2024-09-15T14:19:10.602319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

first
Text

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:11.079783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0804319
Min length3

Characters and Unicode

Total characters7884344
Distinct characters49
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer
2nd rowStephanie
3rd rowEdward
4th rowJeremy
5th rowTyler
ValueCountFrequency (%)
christopher 26669
 
2.1%
robert 21667
 
1.7%
jessica 20581
 
1.6%
james 20039
 
1.5%
michael 20009
 
1.5%
david 19965
 
1.5%
jennifer 16940
 
1.3%
william 16371
 
1.3%
mary 16346
 
1.3%
john 16325
 
1.3%
Other values (342) 1101763
85.0%
2024-09-15T14:19:11.666066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6587669
83.6%
Uppercase Letter 1296675
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1007700
15.3%
e 860878
13.1%
i 618247
9.4%
n 614453
9.3%
r 607072
9.2%
l 388220
 
5.9%
h 344993
 
5.2%
s 324237
 
4.9%
t 311569
 
4.7%
o 268849
 
4.1%
Other values (16) 1241451
18.8%
Uppercase Letter
ValueCountFrequency (%)
J 218907
16.9%
M 144916
11.2%
S 114469
8.8%
A 112464
8.7%
C 106121
8.2%
D 86078
 
6.6%
K 85426
 
6.6%
R 70457
 
5.4%
T 66590
 
5.1%
L 62879
 
4.8%
Other values (13) 228368
17.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7884344
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7884344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

last
Text

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:12.041403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1111774
Min length2

Characters and Unicode

Total characters7924211
Distinct characters48
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowGill
3rd rowSanchez
4th rowWhite
5th rowGarcia
ValueCountFrequency (%)
smith 28794
 
2.2%
williams 23605
 
1.8%
davis 21910
 
1.7%
johnson 20034
 
1.5%
rodriguez 17394
 
1.3%
martinez 14805
 
1.1%
jones 13976
 
1.1%
lewis 12753
 
1.0%
gonzalez 11799
 
0.9%
miller 11698
 
0.9%
Other values (471) 1119907
86.4%
2024-09-15T14:19:12.552880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6627536
83.6%
Uppercase Letter 1296675
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 786302
11.9%
r 658748
9.9%
a 648005
9.8%
n 609178
9.2%
o 583517
8.8%
l 489180
 
7.4%
s 487668
 
7.4%
i 435378
 
6.6%
t 288591
 
4.4%
h 228981
 
3.5%
Other values (15) 1411988
21.3%
Uppercase Letter
ValueCountFrequency (%)
M 158701
12.2%
W 106490
 
8.2%
S 105221
 
8.1%
C 93308
 
7.2%
B 84092
 
6.5%
R 83194
 
6.4%
H 81444
 
6.3%
G 75241
 
5.8%
J 71781
 
5.5%
P 66087
 
5.1%
Other values (13) 371116
28.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7924211
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7924211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Length

2024-09-15T14:19:12.700975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T14:19:12.823931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 709863
54.7%
m 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1296675
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1296675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

street
Text

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:13.172463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.229027
Min length12

Characters and Unicode

Total characters28823823
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest
ValueCountFrequency (%)
apt 327791
 
6.4%
suite 305467
 
5.9%
island 22954
 
0.4%
michael 18967
 
0.4%
common 17978
 
0.3%
station 17957
 
0.3%
islands 17917
 
0.3%
david 17476
 
0.3%
brooks 16991
 
0.3%
fields 16321
 
0.3%
Other values (1940) 4376722
84.9%
2024-09-15T14:19:13.682125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14413030
50.0%
Decimal Number 6996528
24.3%
Space Separator 3859866
 
13.4%
Uppercase Letter 3226608
 
11.2%
Other Punctuation 327791
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1792676
12.4%
a 1454190
10.1%
i 1296969
9.0%
t 1248091
8.7%
r 1103208
 
7.7%
n 1066149
 
7.4%
s 1034564
 
7.2%
l 889594
 
6.2%
o 875571
 
6.1%
u 613916
 
4.3%
Other values (16) 3038102
21.1%
Uppercase Letter
ValueCountFrequency (%)
S 561924
17.4%
A 421707
13.1%
M 258180
 
8.0%
C 223839
 
6.9%
P 195864
 
6.1%
R 186303
 
5.8%
B 148676
 
4.6%
F 143149
 
4.4%
L 131665
 
4.1%
J 121164
 
3.8%
Other values (14) 834137
25.9%
Decimal Number
ValueCountFrequency (%)
5 748812
10.7%
3 739928
10.6%
2 734719
10.5%
7 703124
10.0%
1 693880
9.9%
8 692585
9.9%
6 677709
9.7%
0 677245
9.7%
4 669799
9.6%
9 658727
9.4%
Space Separator
ValueCountFrequency (%)
3859866
100.0%
Other Punctuation
ValueCountFrequency (%)
. 327791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17639638
61.2%
Common 11184185
38.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1792676
 
10.2%
a 1454190
 
8.2%
i 1296969
 
7.4%
t 1248091
 
7.1%
r 1103208
 
6.3%
n 1066149
 
6.0%
s 1034564
 
5.9%
l 889594
 
5.0%
o 875571
 
5.0%
u 613916
 
3.5%
Other values (40) 6264710
35.5%
Common
ValueCountFrequency (%)
3859866
34.5%
5 748812
 
6.7%
3 739928
 
6.6%
2 734719
 
6.6%
7 703124
 
6.3%
1 693880
 
6.2%
8 692585
 
6.2%
6 677709
 
6.1%
0 677245
 
6.1%
4 669799
 
6.0%
Other values (2) 986518
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28823823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

city
Text

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:14.103722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6522459
Min length3

Characters and Unicode

Total characters11219151
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian Falls
2nd rowOrient
3rd rowMalad City
4th rowBoulder
5th rowDoe Hill
ValueCountFrequency (%)
city 21314
 
1.3%
west 19473
 
1.2%
north 14425
 
0.9%
saint 14363
 
0.9%
falls 12794
 
0.8%
new 11842
 
0.7%
mount 11375
 
0.7%
lake 11249
 
0.7%
san 10260
 
0.6%
springs 8727
 
0.5%
Other values (918) 1482445
91.6%
2024-09-15T14:19:14.637673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9277246
82.7%
Uppercase Letter 1619290
 
14.4%
Space Separator 321592
 
2.9%
Dash Punctuation 1023
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1090254
11.8%
a 935089
10.1%
n 821831
8.9%
o 817806
8.8%
l 781662
 
8.4%
r 748921
 
8.1%
i 704285
 
7.6%
t 598490
 
6.5%
s 446306
 
4.8%
d 309005
 
3.3%
Other values (15) 2023597
21.8%
Uppercase Letter
ValueCountFrequency (%)
C 156587
 
9.7%
M 147711
 
9.1%
S 136036
 
8.4%
B 133396
 
8.2%
H 115641
 
7.1%
W 95433
 
5.9%
P 92084
 
5.7%
L 86511
 
5.3%
R 79150
 
4.9%
A 74999
 
4.6%
Other values (15) 501742
31.0%
Space Separator
ValueCountFrequency (%)
321592
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10896536
97.1%
Common 322615
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1090254
 
10.0%
a 935089
 
8.6%
n 821831
 
7.5%
o 817806
 
7.5%
l 781662
 
7.2%
r 748921
 
6.9%
i 704285
 
6.5%
t 598490
 
5.5%
s 446306
 
4.1%
d 309005
 
2.8%
Other values (40) 3642887
33.4%
Common
ValueCountFrequency (%)
321592
99.7%
- 1023
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11219151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:14.896496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2593350
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA
ValueCountFrequency (%)
tx 94876
 
7.3%
ny 83501
 
6.4%
pa 79847
 
6.2%
ca 56360
 
4.3%
oh 46480
 
3.6%
mi 46154
 
3.6%
il 43252
 
3.3%
fl 42671
 
3.3%
al 40989
 
3.2%
mo 38403
 
3.0%
Other values (41) 724142
55.8%
2024-09-15T14:19:15.271314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2593350
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2593350
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2593350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:15.426699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2024-09-15T14:19:15.588367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 5496
 
0.4%
1595797 5130
 
0.4%
1312922 5075
 
0.4%
1766 4574
 
0.4%
241 4533
 
0.3%
2906700 4168
 
0.3%
276002 4155
 
0.3%
302 4147
 
0.3%
910148 4073
 
0.3%
198 4067
 
0.3%
Other values (869) 1251257
96.5%
ValueCountFrequency (%)
23 2049
0.2%
37 1013
 
0.1%
43 2034
0.2%
46 3040
0.2%
47 511
 
< 0.1%
49 1054
 
0.1%
51 1016
 
0.1%
52 518
 
< 0.1%
53 2610
0.2%
60 1045
 
0.1%
ValueCountFrequency (%)
2906700 4168
0.3%
2504700 2033
 
0.2%
2383912 521
 
< 0.1%
1595797 5130
0.4%
1577385 2563
0.2%
1526206 3517
0.3%
1417793 8
 
< 0.1%
1382480 2056
0.2%
1312922 5075
0.4%
1263321 3629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:15.901513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer 131756
 
4.6%
officer 110915
 
3.9%
manager 61124
 
2.1%
scientist 55878
 
1.9%
designer 52218
 
1.8%
surveyor 49062
 
1.7%
teacher 38126
 
1.3%
psychologist 32600
 
1.1%
research 29754
 
1.0%
editor 28725
 
1.0%
Other values (456) 2289024
79.5%
2024-09-15T14:19:16.377338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22784440
86.9%
Space Separator 1582507
 
6.0%
Uppercase Letter 1369269
 
5.2%
Other Punctuation 443484
 
1.7%
Close Punctuation 24139
 
0.1%
Open Punctuation 24139
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2803032
12.3%
i 2386346
10.5%
r 2198669
9.6%
a 1813638
 
8.0%
t 1782302
 
7.8%
n 1764769
 
7.7%
o 1491775
 
6.5%
s 1444701
 
6.3%
c 1323152
 
5.8%
l 999624
 
4.4%
Other values (16) 4776432
21.0%
Uppercase Letter
ValueCountFrequency (%)
C 156704
11.4%
E 145426
10.6%
P 143111
10.5%
S 137500
10.0%
T 113148
 
8.3%
M 89545
 
6.5%
A 88466
 
6.5%
F 68651
 
5.0%
D 58034
 
4.2%
R 55841
 
4.1%
Other values (11) 312843
22.8%
Other Punctuation
ValueCountFrequency (%)
, 312210
70.4%
/ 123567
 
27.9%
' 7707
 
1.7%
Space Separator
ValueCountFrequency (%)
1582507
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24153709
92.1%
Common 2074269
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2803032
11.6%
i 2386346
 
9.9%
r 2198669
 
9.1%
a 1813638
 
7.5%
t 1782302
 
7.4%
n 1764769
 
7.3%
o 1491775
 
6.2%
s 1444701
 
6.0%
c 1323152
 
5.5%
l 999624
 
4.1%
Other values (37) 6145701
25.4%
Common
ValueCountFrequency (%)
1582507
76.3%
, 312210
 
15.1%
/ 123567
 
6.0%
) 24139
 
1.2%
( 24139
 
1.2%
' 7707
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26227978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

unix_time
Real number (ℝ)

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:16.538072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2024-09-15T14:19:16.693312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:16.841391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2024-09-15T14:19:16.996955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-09-15T14:19:17.146978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2024-09-15T14:19:17.296304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2024-09-15T14:19:17.452029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T14:19:17.567512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

day
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.587978
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:17.673892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8291214
Coefficient of variation (CV)0.5664058
Kurtosis-1.1871417
Mean15.587978
Median Absolute Deviation (MAD)8
Skewness0.030847364
Sum20212542
Variance77.953384
MonotonicityNot monotonic
2024-09-15T14:19:17.803197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 47089
 
3.6%
15 46213
 
3.6%
8 46201
 
3.6%
16 44894
 
3.5%
2 44748
 
3.5%
9 44685
 
3.4%
7 44239
 
3.4%
14 44015
 
3.4%
28 43470
 
3.4%
17 42272
 
3.3%
Other values (21) 848849
65.5%
ValueCountFrequency (%)
1 47089
3.6%
2 44748
3.5%
3 41842
3.2%
4 41479
3.2%
5 41886
3.2%
6 41420
3.2%
7 44239
3.4%
8 46201
3.6%
9 44685
3.4%
10 41934
3.2%
ValueCountFrequency (%)
31 24701
1.9%
30 41019
3.2%
29 39617
3.1%
28 43470
3.4%
27 39684
3.1%
26 40692
3.1%
25 40374
3.1%
24 41360
3.2%
23 40815
3.1%
22 42061
3.2%

day_of_week
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0706037
Minimum0
Maximum6
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:17.912005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1981526
Coefficient of variation (CV)0.71586984
Kurtosis-1.445049
Mean3.0706037
Median Absolute Deviation (MAD)2
Skewness-0.078453041
Sum3981575
Variance4.8318747
MonotonicityNot monotonic
2024-09-15T14:19:18.015836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 254282
19.6%
6 250579
19.3%
5 200957
15.5%
1 160227
12.4%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
ValueCountFrequency (%)
0 254282
19.6%
1 160227
12.4%
2 131073
10.1%
3 147285
11.4%
4 152272
11.7%
5 200957
15.5%
6 250579
19.3%
ValueCountFrequency (%)
6 250579
19.3%
5 200957
15.5%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
1 160227
12.4%
0 254282
19.6%

age
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.742545
Minimum15
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:18.157217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile23
Q133
median45
Q358
95-th percentile81
Maximum96
Range81
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.378485
Coefficient of variation (CV)0.37179158
Kurtosis-0.1763463
Mean46.742545
Median Absolute Deviation (MAD)12
Skewness0.61235845
Sum60609890
Variance302.01173
MonotonicityNot monotonic
2024-09-15T14:19:18.315823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 45483
 
3.5%
36 40038
 
3.1%
33 37481
 
2.9%
35 37313
 
2.9%
46 34299
 
2.6%
44 32706
 
2.5%
34 31851
 
2.5%
30 31386
 
2.4%
47 31271
 
2.4%
32 30718
 
2.4%
Other values (71) 944129
72.8%
ValueCountFrequency (%)
15 1959
 
0.2%
16 7496
 
0.6%
17 3975
 
0.3%
19 5603
 
0.4%
20 9530
 
0.7%
21 18827
1.5%
22 13241
1.0%
23 29689
2.3%
24 6008
 
0.5%
25 20573
1.6%
ValueCountFrequency (%)
96 536
 
< 0.1%
95 11
 
< 0.1%
94 6063
0.5%
93 4645
0.4%
92 4131
0.3%
91 6224
0.5%
90 3605
0.3%
89 4610
0.4%
88 2096
 
0.2%
87 3041
0.2%

merchant_encoded
Real number (ℝ)

HIGH CORRELATION 

Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.85849
Minimum0
Maximum692
Zeros1844
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:18.475974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1165
median346
Q3514
95-th percentile659
Maximum692
Range692
Interquartile range (IQR)349

Descriptive statistics

Standard deviation200.9519
Coefficient of variation (CV)0.58610739
Kurtosis-1.215053
Mean342.85849
Median Absolute Deviation (MAD)175
Skewness0.0086598641
Sum4.4457604 × 108
Variance40381.666
MonotonicityNot monotonic
2024-09-15T14:19:18.648052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316 4403
 
0.3%
105 3649
 
0.3%
571 3634
 
0.3%
349 3510
 
0.3%
70 3493
 
0.3%
136 3434
 
0.3%
117 2736
 
0.2%
358 2734
 
0.2%
463 2723
 
0.2%
607 2721
 
0.2%
Other values (683) 1263638
97.5%
ValueCountFrequency (%)
0 1844
0.1%
1 1763
0.1%
2 1751
0.1%
3 1895
0.1%
4 940
 
0.1%
5 1746
0.1%
6 1904
0.1%
7 2503
0.2%
8 1923
0.1%
9 821
 
0.1%
ValueCountFrequency (%)
692 1783
0.1%
691 2560
0.2%
690 1695
0.1%
689 1804
0.1%
688 1297
0.1%
687 2017
0.2%
686 1870
0.1%
685 1766
0.1%
684 1872
0.1%
683 2358
0.2%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.804858
Minimum0
Maximum23
Zeros42502
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:18.796102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8178239
Coefficient of variation (CV)0.53244042
Kurtosis-1.0795803
Mean12.804858
Median Absolute Deviation (MAD)5
Skewness-0.28282545
Sum16603739
Variance46.482723
MonotonicityNot monotonic
2024-09-15T14:19:18.923739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 67104
 
5.2%
22 66982
 
5.2%
18 66051
 
5.1%
16 65726
 
5.1%
21 65533
 
5.1%
19 65508
 
5.1%
17 65450
 
5.0%
15 65391
 
5.0%
13 65314
 
5.0%
12 65257
 
5.0%
Other values (14) 638359
49.2%
ValueCountFrequency (%)
0 42502
3.3%
1 42869
3.3%
2 42656
3.3%
3 42769
3.3%
4 41863
3.2%
5 42171
3.3%
6 42300
3.3%
7 42203
3.3%
8 42505
3.3%
9 42185
3.3%
ValueCountFrequency (%)
23 67104
5.2%
22 66982
5.2%
21 65533
5.1%
20 65098
5.0%
19 65508
5.1%
18 66051
5.1%
17 65450
5.0%
16 65726
5.1%
15 65391
5.0%
14 64885
5.0%

time_diff
Real number (ℝ)

Distinct158975
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32460.389
Minimum0
Maximum1341471
Zeros1003
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:19.064108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile969
Q16004
median16563
Q340239
95-th percentile113905
Maximum1341471
Range1341471
Interquartile range (IQR)34235

Descriptive statistics

Standard deviation47331.145
Coefficient of variation (CV)1.4581201
Kurtosis31.873749
Mean32460.389
Median Absolute Deviation (MAD)12924
Skewness4.2732438
Sum4.2090574 × 1010
Variance2.2402373 × 109
MonotonicityNot monotonic
2024-09-15T14:19:19.235897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1003
 
0.1%
221 91
 
< 0.1%
290 90
 
< 0.1%
118 90
 
< 0.1%
445 89
 
< 0.1%
572 88
 
< 0.1%
821 87
 
< 0.1%
11 87
 
< 0.1%
136 86
 
< 0.1%
379 86
 
< 0.1%
Other values (158965) 1294878
99.9%
ValueCountFrequency (%)
0 1003
0.1%
1 62
 
< 0.1%
2 81
 
< 0.1%
3 76
 
< 0.1%
4 57
 
< 0.1%
5 67
 
< 0.1%
6 67
 
< 0.1%
7 64
 
< 0.1%
8 72
 
< 0.1%
9 72
 
< 0.1%
ValueCountFrequency (%)
1341471 1
< 0.1%
1205687 1
< 0.1%
1107569 1
< 0.1%
1096094 1
< 0.1%
1060731 1
< 0.1%
1053269 1
< 0.1%
1045690 1
< 0.1%
1039152 1
< 0.1%
1032247 1
< 0.1%
1016241 1
< 0.1%

day_of_month
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.589412
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T14:19:19.385675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8312176
Coefficient of variation (CV)0.56648818
Kurtosis-1.1868502
Mean15.589412
Median Absolute Deviation (MAD)8
Skewness0.031380011
Sum20214401
Variance77.990405
MonotonicityNot monotonic
2024-09-15T14:19:19.532630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 47089
 
3.6%
15 46213
 
3.6%
8 46201
 
3.6%
16 44894
 
3.5%
2 44748
 
3.5%
9 44685
 
3.4%
7 44239
 
3.4%
14 44015
 
3.4%
17 42272
 
3.3%
22 42061
 
3.2%
Other values (21) 850258
65.6%
ValueCountFrequency (%)
1 47089
3.6%
2 44748
3.5%
3 41842
3.2%
4 41479
3.2%
5 41886
3.2%
6 41420
3.2%
7 44239
3.4%
8 46201
3.6%
9 44685
3.4%
10 41934
3.2%
ValueCountFrequency (%)
31 24701
1.9%
30 41019
3.2%
29 41476
3.2%
28 41611
3.2%
27 39684
3.1%
26 40692
3.1%
25 40374
3.1%
24 41360
3.2%
23 40815
3.1%
22 42061
3.2%

is_weekend
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
845139 
1
451536 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Length

2024-09-15T14:19:19.663124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T14:19:19.778802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring characters

ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

amt_ratio
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1166530
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0.0086933687
Maximum386.48159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:19.916188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0086933687
5-th percentile0.037613054
Q10.15441537
median0.66941509
Q31.187412
95-th percentile2.620873
Maximum386.48159
Range386.47289
Interquartile range (IQR)1.0329966

Descriptive statistics

Standard deviation2.2999175
Coefficient of variation (CV)2.2999175
Kurtosis2845.425
Mean1
Median Absolute Deviation (MAD)0.51563683
Skewness34.809554
Sum1296675
Variance5.2896205
MonotonicityNot monotonic
2024-09-15T14:19:20.076718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1697644044 9
 
< 0.1%
0.1066987452 9
 
< 0.1%
0.08787319111 8
 
< 0.1%
0.0790465957 8
 
< 0.1%
0.1549432174 8
 
< 0.1%
0.04983253947 7
 
< 0.1%
0.08567179375 7
 
< 0.1%
0.05196871342 7
 
< 0.1%
0.07718171096 7
 
< 0.1%
0.02356862305 7
 
< 0.1%
Other values (1166520) 1296598
> 99.9%
ValueCountFrequency (%)
0.008693368698 1
< 0.1%
0.00877907162 1
< 0.1%
0.00929940348 1
< 0.1%
0.009387522129 1
< 0.1%
0.009534706331 1
< 0.1%
0.009548024185 2
< 0.1%
0.009568974574 1
< 0.1%
0.009641632266 1
< 0.1%
0.009828848426 1
< 0.1%
0.009856043811 1
< 0.1%
ValueCountFrequency (%)
386.4815883 1
< 0.1%
358.5962394 1
< 0.1%
283.0207865 1
< 0.1%
248.4744115 1
< 0.1%
236.1516822 1
< 0.1%
235.729112 1
< 0.1%
235.4992914 1
< 0.1%
233.4279738 1
< 0.1%
219.4420112 1
< 0.1%
217.5342348 1
< 0.1%

high_value
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1231853 
1
 
64822

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Length

2024-09-15T14:19:20.218225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T14:19:20.319007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

category_food_dining
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1205214 
True
 
91461
ValueCountFrequency (%)
False 1205214
92.9%
True 91461
 
7.1%
2024-09-15T14:19:20.408090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_gas_transport
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1165016 
True
131659 
ValueCountFrequency (%)
False 1165016
89.8%
True 131659
 
10.2%
2024-09-15T14:19:20.502344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_grocery_net
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1251223 
True
 
45452
ValueCountFrequency (%)
False 1251223
96.5%
True 45452
 
3.5%
2024-09-15T14:19:20.599240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_grocery_pos
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1173037 
True
123638 
ValueCountFrequency (%)
False 1173037
90.5%
True 123638
 
9.5%
2024-09-15T14:19:20.689927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_health_fitness
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1210796 
True
 
85879
ValueCountFrequency (%)
False 1210796
93.4%
True 85879
 
6.6%
2024-09-15T14:19:21.451197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_home
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1173560 
True
123115 
ValueCountFrequency (%)
False 1173560
90.5%
True 123115
 
9.5%
2024-09-15T14:19:21.547017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_kids_pets
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1183640 
True
 
113035
ValueCountFrequency (%)
False 1183640
91.3%
True 113035
 
8.7%
2024-09-15T14:19:21.643166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_misc_net
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1233388 
True
 
63287
ValueCountFrequency (%)
False 1233388
95.1%
True 63287
 
4.9%
2024-09-15T14:19:21.749621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_misc_pos
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1217020 
True
 
79655
ValueCountFrequency (%)
False 1217020
93.9%
True 79655
 
6.1%
2024-09-15T14:19:21.849470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_personal_care
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1205917 
True
 
90758
ValueCountFrequency (%)
False 1205917
93.0%
True 90758
 
7.0%
2024-09-15T14:19:21.951152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_shopping_net
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1199132 
True
 
97543
ValueCountFrequency (%)
False 1199132
92.5%
True 97543
 
7.5%
2024-09-15T14:19:22.058279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_shopping_pos
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1180003 
True
 
116672
ValueCountFrequency (%)
False 1180003
91.0%
True 116672
 
9.0%
2024-09-15T14:19:22.160528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

category_travel
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
False
1256168 
True
 
40507
ValueCountFrequency (%)
False 1256168
96.9%
True 40507
 
3.1%
2024-09-15T14:19:22.268238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

amt_merchant_interaction
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct850172
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24035.621
Minimum0
Maximum15748202
Zeros1844
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:22.404498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile382.3
Q12552.1
median10352.25
Q329331.085
95-th percentile77275.25
Maximum15748202
Range15748202
Interquartile range (IQR)26778.985

Descriptive statistics

Standard deviation64788.516
Coefficient of variation (CV)2.6955208
Kurtosis6863.1684
Mean24035.621
Median Absolute Deviation (MAD)9173.73
Skewness50.346708
Sum3.1166389 × 1010
Variance4.1975518 × 109
MonotonicityNot monotonic
2024-09-15T14:19:22.567983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1844
 
0.1%
672 36
 
< 0.1%
1224 36
 
< 0.1%
756 35
 
< 0.1%
693 32
 
< 0.1%
1344 31
 
< 0.1%
1056 31
 
< 0.1%
1512 31
 
< 0.1%
1008 30
 
< 0.1%
315 30
 
< 0.1%
Other values (850162) 1294539
99.8%
ValueCountFrequency (%)
0 1844
0.1%
1 1
 
< 0.1%
1.01 1
 
< 0.1%
1.03 1
 
< 0.1%
1.04 1
 
< 0.1%
1.05 2
 
< 0.1%
1.06 2
 
< 0.1%
1.1 1
 
< 0.1%
1.11 3
 
< 0.1%
1.13 1
 
< 0.1%
ValueCountFrequency (%)
15748201.6 1
< 0.1%
11832531.84 1
< 0.1%
11715740.64 1
< 0.1%
11114186.04 1
< 0.1%
7866051.3 1
< 0.1%
7668280.95 1
< 0.1%
7448548.25 1
< 0.1%
7414617.32 1
< 0.1%
7126857.95 1
< 0.1%
6743415.28 1
< 0.1%

amt_day_interaction
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct99344
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.66738
Minimum0
Maximum173693.4
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:22.734000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.25
median73.29
Q3269.75
95-th percentile741.42
Maximum173693.4
Range173693.4
Interquartile range (IQR)261.5

Descriptive statistics

Standard deviation631.42542
Coefficient of variation (CV)2.9277743
Kurtosis10492.695
Mean215.66738
Median Absolute Deviation (MAD)73.29
Skewness62.287145
Sum2.796505 × 108
Variance398698.06
MonotonicityNot monotonic
2024-09-15T14:19:22.894872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254282
 
19.6%
9 355
 
< 0.1%
7.5 334
 
< 0.1%
10.2 314
 
< 0.1%
12 313
 
< 0.1%
10.8 311
 
< 0.1%
6.24 301
 
< 0.1%
6 296
 
< 0.1%
9.48 291
 
< 0.1%
15 288
 
< 0.1%
Other values (99334) 1039590
80.2%
ValueCountFrequency (%)
0 254282
19.6%
1 36
 
< 0.1%
1.01 67
 
< 0.1%
1.02 62
 
< 0.1%
1.03 70
 
< 0.1%
1.04 53
 
< 0.1%
1.05 59
 
< 0.1%
1.06 60
 
< 0.1%
1.07 55
 
< 0.1%
1.08 65
 
< 0.1%
ValueCountFrequency (%)
173693.4 1
< 0.1%
135598.85 1
< 0.1%
132720.6 1
< 0.1%
107383.44 1
< 0.1%
100347.76 1
< 0.1%
90282.18 1
< 0.1%
76725.06 1
< 0.1%
72338.2 1
< 0.1%
72036.25 1
< 0.1%
67684.2 1
< 0.1%

amt_mean
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum42.951671
Maximum948.81818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:23.047665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.951671
5-th percentile52.795807
Q159.813649
median65.09374
Q383.277582
95-th percentile96.281225
Maximum948.81818
Range905.86651
Interquartile range (IQR)23.463933

Descriptive statistics

Standard deviation19.410291
Coefficient of variation (CV)0.27590625
Kurtosis459.14005
Mean70.351035
Median Absolute Deviation (MAD)7.1445792
Skewness14.40415
Sum91222429
Variance376.75938
MonotonicityNot monotonic
2024-09-15T14:19:23.200108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.45309958 3123
 
0.2%
55.35335575 3123
 
0.2%
89.77494389 3119
 
0.2%
57.5451941 3117
 
0.2%
48.47894957 3113
 
0.2%
52.44669344 3112
 
0.2%
95.31727653 3110
 
0.2%
52.78476344 3107
 
0.2%
91.44027688 3106
 
0.2%
89.6934118 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
42.951671 1538
0.1%
44.71734531 501
 
< 0.1%
46.88654145 1544
0.1%
46.93667091 1571
0.1%
46.98265945 2587
0.2%
47.17054602 2564
0.2%
47.18054799 3011
0.2%
47.94510046 2588
0.2%
48.47894957 3113
0.2%
48.48821547 2107
0.2%
ValueCountFrequency (%)
948.8181818 11
< 0.1%
918.4255556 9
< 0.1%
874.5057143 7
< 0.1%
858.48 8
< 0.1%
842.23125 8
< 0.1%
833.969 10
< 0.1%
810.2785714 7
< 0.1%
799.2133333 9
< 0.1%
778.5718182 11
< 0.1%
774.74375 8
< 0.1%

amt_std
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.34551
Minimum60.247108
Maximum1202.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T14:19:23.365547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60.247108
5-th percentile83.708636
Q1103.59066
median122.37808
Q3149.71688
95-th percentile266.56177
Maximum1202.988
Range1142.7409
Interquartile range (IQR)46.126219

Descriptive statistics

Standard deviation73.303097
Coefficient of variation (CV)0.51860931
Kurtosis46.700004
Mean141.34551
Median Absolute Deviation (MAD)21.853523
Skewness5.0391525
Sum1.8327919 × 108
Variance5373.344
MonotonicityNot monotonic
2024-09-15T14:19:23.519241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143.1626102 3123
 
0.2%
121.6611149 3123
 
0.2%
118.4502102 3119
 
0.2%
211.4021978 3117
 
0.2%
131.566918 3113
 
0.2%
155.4128126 3112
 
0.2%
216.5906992 3110
 
0.2%
140.3830389 3107
 
0.2%
132.7622028 3106
 
0.2%
137.017678 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
60.24710813 504
 
< 0.1%
64.15872518 471
 
< 0.1%
65.34697836 518
 
< 0.1%
65.53295778 972
0.1%
65.84396856 496
 
< 0.1%
66.98899721 525
 
< 0.1%
67.49926144 1494
0.1%
70.82320784 1005
0.1%
71.420119 485
 
< 0.1%
72.37512206 509
 
< 0.1%
ValueCountFrequency (%)
1202.988005 510
 
< 0.1%
1165.824421 520
 
< 0.1%
867.2878545 1017
 
0.1%
644.2690528 2050
0.2%
623.2522115 540
 
< 0.1%
543.2532612 13
 
< 0.1%
512.7832113 550
 
< 0.1%
510.8874515 2597
0.2%
483.3276455 1060
0.1%
482.145942 10
 
< 0.1%

Interactions

2024-09-15T14:18:56.889615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:42.880623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:47.638527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:52.099045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:56.265283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:00.613377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:04.892150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:09.167558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:14.273421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:19.108123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:23.777297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:28.174268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:32.828658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:37.303968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:41.822376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:47.372517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:52.177265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:57.163616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:43.201911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:47.887185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:52.332919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:56.510297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:00.858846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:05.148145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:09.416989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:14.547063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:19.389184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:24.071437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:28.432222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:33.125536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:37.551604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:42.777296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:47.655135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:52.419732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:57.439324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:43.488820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:48.156140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:52.567074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:56.749850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:01.127064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:05.387866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:09.666013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:14.833441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:19.656298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:24.333672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:28.719282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:33.386719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:37.801980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:43.067885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:47.925850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:52.676709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:57.694988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:43.768089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:48.386106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:52.812019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:57.023897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:01.367334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:05.648580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:09.914928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:15.114267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:19.931069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:24.569395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:29.022681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:33.698275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:38.073383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:43.328144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:48.197310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:52.950471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:57.959976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:44.032722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:48.604044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:53.074816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:57.248075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:01.592708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:05.886180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:10.222844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:15.365745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:20.198493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:24.805522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:29.273949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:33.977979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:38.299316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:43.613543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:48.454640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:53.223192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:58.214559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:44.274754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:48.830207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:53.289340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:57.475223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:01.817169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:06.118492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:10.503177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:15.668881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:20.455375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:25.079759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:29.545345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:34.222844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:38.526856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:43.880664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:48.735900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:53.488719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:58.465825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:44.520905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:49.142912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:53.527672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:57.739408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:02.085861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:06.342847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:10.756919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:15.956918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:20.700996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:25.352422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:29.822476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:34.464139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:38.770754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:44.151294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:49.064699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:53.741019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:58.723542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:44.751377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:49.384007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:53.766911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:58.038154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:02.321667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:06.577725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:11.494228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:16.228599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:21.008780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:25.611305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:30.117965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:34.716742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:39.039936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:44.413696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:49.323461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:54.025060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:59.019193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:45.023283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:49.675610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:54.048757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:58.305704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:02.570040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:06.822887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:11.731992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:16.498115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:21.263490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:25.868461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:30.382523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:35.023636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:39.281453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:44.724016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:49.607084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:54.288107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:59.266318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:45.266461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:49.983759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:54.282066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:58.572743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:02.823912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:07.105080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:12.044385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:16.796628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:21.525242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:26.142570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:30.658456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:35.281466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:39.519411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:45.031720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:49.890042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:54.545120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:59.522563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:45.496986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:50.237452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:54.504031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:58.817322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:03.091642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:07.334075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:12.321341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:17.075682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:21.788380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:26.379304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:30.914023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:35.515584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:39.749966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:45.310000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:50.171809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:54.814673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:59.792146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:45.737947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:50.501091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:54.747994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:59.083595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:03.332525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:07.557761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:12.578315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:17.407719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:22.099014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:26.631109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:31.198484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:35.789892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:40.047564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:45.616858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:50.453026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:55.104464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:19:00.071213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:45.991683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:50.781099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:55.028019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:59.325374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:03.586046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:07.784090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:12.841809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:17.672246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:22.376215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:26.873072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:31.460554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:36.080971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:40.301313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:45.923165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:50.718278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:55.397311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:19:00.337817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:46.663085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:51.085762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:55.266614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:59.575205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:03.850249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:08.083334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:13.150481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:17.985263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:22.659342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:27.156475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:31.745570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:36.322708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:40.582417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:46.215490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:51.057493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:55.720316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:19:00.611778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:46.915650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:51.350337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:55.515134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:59.831544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:04.124963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:08.340855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:13.425474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:18.251007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:22.985925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:27.421412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:32.050719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:36.574124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:40.869118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:46.534748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:51.381155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:56.073269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:19:00.877274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:47.161079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:51.604048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:55.761364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:00.120926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:04.389288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:08.603108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:13.733654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:18.559860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:23.269730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:27.672554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:32.318656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:36.816048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:41.221450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:46.808069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:51.659406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:56.345599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:19:01.146167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:47.399536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:51.845249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:17:56.027316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:00.366105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:04.657240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:08.888182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:14.018345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:18.814770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:23.519947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:27.915603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:32.568315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:37.072418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:41.528039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:47.104639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:51.911227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:18:56.625203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T14:19:23.669891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ageamtamt_day_interactionamt_meanamt_merchant_interactionamt_ratioamt_stdcategory_food_diningcategory_gas_transportcategory_grocery_netcategory_grocery_poscategory_health_fitnesscategory_homecategory_kids_petscategory_misc_netcategory_misc_poscategory_personal_carecategory_shopping_netcategory_shopping_poscategory_travelcity_popdayday_of_monthday_of_weekgenderhigh_valuehouris_fraudis_weekendmerch_latmerch_longmerchant_encodedtime_diffunix_time
age1.000-0.024-0.0220.009-0.025-0.006-0.0580.0250.0650.0870.0470.0100.0440.0230.0210.0320.0230.0190.0210.027-0.1570.0010.001-0.0130.1320.043-0.1730.0200.0460.036-0.020-0.0070.125-0.004
amt-0.0241.0000.5790.2270.8110.979-0.0860.0040.0050.0020.0050.0040.0050.0050.0030.0040.0040.0090.0110.053-0.0240.0000.000-0.0010.0000.074-0.1540.0000.0010.0120.000-0.0120.0300.001
amt_day_interaction-0.0220.5791.0000.1310.4790.570-0.0510.0010.0020.0000.0020.0010.0020.0020.0000.0010.0010.0030.0050.038-0.0150.0180.0180.6840.0010.046-0.0880.0000.0080.0080.001-0.0060.062-0.018
amt_mean0.0090.2270.1311.0000.1880.0570.0540.0050.0040.0020.0150.0060.0070.0060.0120.0030.0050.0210.0030.0010.097-0.000-0.000-0.0030.0100.085-0.0440.3130.005-0.0250.0070.0040.0640.002
amt_merchant_interaction-0.0250.8110.4790.1881.0000.798-0.0690.0030.0030.0010.0030.0020.0030.0030.0000.0020.0030.0070.0060.039-0.0170.0000.000-0.0000.0000.053-0.1340.0000.0000.009-0.0010.5000.026-0.000
amt_ratio-0.0060.9790.5700.0570.7981.000-0.0760.0050.0060.0030.0060.0050.0060.0060.0020.0040.0050.0140.0160.052-0.0270.0000.0000.0000.0000.086-0.1660.0000.0010.011-0.001-0.0130.0250.000
amt_std-0.058-0.086-0.0510.054-0.069-0.0761.0000.0030.0140.0110.0050.0050.0050.0040.0030.0040.0030.0040.0050.0040.2290.0010.001-0.0020.1120.0080.0380.0440.003-0.0690.0370.002-0.040-0.003
category_food_dining0.0250.0040.0010.0050.0030.0050.0031.0000.0930.0520.0890.0730.0890.0850.0620.0700.0760.0790.0870.0490.0050.0000.0000.0030.0100.0420.1600.0150.0010.0020.0070.1050.0010.003
category_gas_transport0.0650.0050.0020.0040.0030.0060.0140.0931.0000.0640.1090.0900.1090.1040.0760.0860.0920.0960.1060.0600.0260.0010.0010.0030.0030.0770.4190.0050.0030.0210.0140.1230.0000.000
category_grocery_net0.0870.0020.0000.0020.0010.0030.0110.0520.0641.0000.0620.0510.0620.0590.0430.0490.0520.0540.0600.0340.0180.0010.0010.0020.0030.0440.2370.0070.0000.0170.0120.0680.0070.000
category_grocery_pos0.0470.0050.0020.0150.0030.0060.0050.0890.1090.0621.0000.0860.1050.1000.0740.0830.0890.0930.1020.0580.0070.0000.0000.0030.0120.0830.3910.0360.0020.0030.0050.1000.0000.003
category_health_fitness0.0100.0040.0010.0060.0020.0050.0050.0730.0900.0510.0861.0000.0860.0820.0600.0680.0730.0760.0840.0480.0030.0010.0010.0010.0110.0380.2140.0150.0020.0050.0040.1400.0000.000
category_home0.0440.0050.0020.0070.0030.0060.0050.0890.1090.0620.1050.0861.0000.1000.0730.0830.0890.0920.1020.0580.0060.0010.0000.0040.0110.0440.2600.0180.0040.0040.0030.0790.0000.001
category_kids_pets0.0230.0050.0020.0060.0030.0060.0040.0850.1040.0590.1000.0820.1001.0000.0700.0790.0850.0880.0970.0550.0000.0010.0010.0020.0050.0420.2480.0150.0020.0030.0010.1370.0000.000
category_misc_net0.0210.0030.0000.0120.0000.0020.0030.0620.0760.0430.0740.0600.0730.0701.0000.0580.0620.0650.0710.0410.0040.0020.0020.0020.0070.0590.1960.0260.0000.0050.0050.1010.0020.002
category_misc_pos0.0320.0040.0010.0030.0020.0040.0040.0700.0860.0490.0830.0680.0830.0790.0581.0000.0700.0730.0800.0460.0030.0000.0000.0040.0080.0400.0940.0090.0040.0040.0080.1390.0020.000
category_personal_care0.0230.0040.0010.0050.0030.0050.0030.0760.0920.0520.0890.0730.0890.0850.0620.0701.0000.0780.0860.0490.0020.0010.0000.0020.0340.0400.2200.0120.0010.0040.0010.0710.0000.000
category_shopping_net0.0190.0090.0030.0210.0070.0140.0040.0790.0960.0540.0930.0760.0920.0880.0650.0730.0781.0000.0900.0510.0090.0020.0020.0000.0110.0640.0190.0440.0000.0030.0060.0980.0030.001
category_shopping_pos0.0210.0110.0050.0030.0060.0160.0050.0870.1060.0600.1020.0840.1020.0970.0710.0800.0860.0901.0000.0560.0110.0020.0020.0000.0210.0470.0030.0060.0000.0090.0050.1320.0020.000
category_travel0.0270.0530.0380.0010.0390.0520.0040.0490.0600.0340.0580.0480.0580.0550.0410.0460.0490.0510.0561.0000.0060.0000.0000.0030.0180.0670.1440.0070.0020.0050.0040.0590.0030.000
city_pop-0.157-0.024-0.0150.097-0.017-0.0270.2290.0050.0260.0180.0070.0030.0060.0000.0040.0030.0020.0090.0110.0061.000-0.001-0.0010.0020.0890.0320.0330.0040.008-0.2640.0860.004-0.009-0.003
day0.0010.0000.018-0.0000.0000.0000.0010.0000.0010.0010.0000.0010.0010.0010.0020.0000.0010.0020.0020.000-0.0011.0001.0000.0170.0000.002-0.0000.0090.070-0.0000.000-0.001-0.0020.019
day_of_month0.0010.0000.018-0.0000.0000.0000.0010.0000.0010.0010.0000.0010.0000.0010.0020.0000.0000.0020.0020.000-0.0011.0001.0000.0170.0000.002-0.0000.0090.069-0.0000.000-0.001-0.0020.019
day_of_week-0.013-0.0010.684-0.003-0.0000.000-0.0020.0030.0030.0020.0030.0010.0040.0020.0020.0040.0020.0000.0000.0030.0020.0170.0171.0000.0060.0020.0000.0121.0000.0000.0010.001-0.011-0.029
gender0.1320.0000.0010.0100.0000.0000.1120.0100.0030.0030.0120.0110.0110.0050.0070.0080.0340.0110.0210.0180.0890.0000.0000.0061.0000.0470.0450.0080.0040.1030.0820.0060.0310.000
high_value0.0430.0740.0460.0850.0530.0860.0080.0420.0770.0440.0830.0380.0440.0420.0590.0400.0400.0640.0470.0670.0320.0020.0020.0020.0471.0000.0300.2490.0010.0220.0130.0230.0070.003
hour-0.173-0.154-0.088-0.044-0.134-0.1660.0380.1600.4190.2370.3910.2140.2600.2480.1960.0940.2200.0190.0030.1440.033-0.000-0.0000.0000.0450.0301.0000.0950.000-0.010-0.006-0.002-0.1200.001
is_fraud0.0200.0000.0000.3130.0000.0000.0440.0150.0050.0070.0360.0150.0180.0150.0260.0090.0120.0440.0060.0070.0040.0090.0090.0120.0080.2490.0951.0000.0040.0080.0050.0100.0100.018
is_weekend0.0460.0010.0080.0050.0000.0010.0030.0010.0030.0000.0020.0020.0040.0020.0000.0040.0010.0000.0000.0020.0080.0700.0691.0000.0040.0010.0000.0041.0000.0040.0050.0010.0250.083
merch_lat0.0360.0120.008-0.0250.0090.011-0.0690.0020.0210.0170.0030.0050.0040.0030.0050.0040.0040.0030.0090.005-0.264-0.000-0.0000.0000.1030.022-0.0100.0080.0041.0000.104-0.0020.0120.001
merch_long-0.0200.0000.0010.007-0.001-0.0010.0370.0070.0140.0120.0050.0040.0030.0010.0050.0080.0010.0060.0050.0040.0860.0000.0000.0010.0820.013-0.0060.0050.0050.1041.000-0.0010.006-0.001
merchant_encoded-0.007-0.012-0.0060.0040.500-0.0130.0020.1050.1230.0680.1000.1400.0790.1370.1010.1390.0710.0980.1320.0590.004-0.001-0.0010.0010.0060.023-0.0020.0100.001-0.002-0.0011.000-0.000-0.001
time_diff0.1250.0300.0620.0640.0260.025-0.0400.0010.0000.0070.0000.0000.0000.0000.0020.0020.0000.0030.0020.003-0.009-0.002-0.002-0.0110.0310.007-0.1200.0100.0250.0120.006-0.0001.000-0.035
unix_time-0.0040.001-0.0180.002-0.0000.000-0.0030.0030.0000.0000.0030.0000.0010.0000.0020.0000.0000.0010.0000.000-0.0030.0190.019-0.0290.0000.0030.0010.0180.0830.001-0.001-0.001-0.0351.000

Missing values

2024-09-15T14:19:01.771837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T14:19:04.344514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

amtfirstlastgenderstreetcitystatecity_popjobunix_timemerch_latmerch_longis_frauddayday_of_weekagemerchant_encodedhourtime_diffday_of_monthis_weekendamt_ratiohigh_valuecategory_food_diningcategory_gas_transportcategory_grocery_netcategory_grocery_poscategory_health_fitnesscategory_homecategory_kids_petscategory_misc_netcategory_misc_poscategory_personal_carecategory_shopping_netcategory_shopping_poscategory_travelamt_merchant_interactionamt_day_interactionamt_meanamt_std
04.97JenniferBanksF561 Perry CoveMoravian FallsNC3495Psychologist, counselling132537601836.011293-82.0483150113251400.0100.0568690FalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalse2554.584.9787.393215126.596221
1107.23StephanieGillF43039 Riley Greens Suite 393OrientWA149Special educational needs teacher132537604449.159047-118.1864620114224100.0101.9876060FalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse25842.43107.2353.949320118.337621
2220.11EdwardSanchezM594 White Dale Suite 530Malad CityID4154Nature conservation officer132537605143.150704-112.1544810115839000.0103.3415801FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse85842.90220.1165.870040101.585754
345.00JeremyWhiteM9443 Cynthia Court Apt. 038BoulderMT1939Patent attorney132537607647.034331-112.5610710115336000.0100.6183300FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse16200.0045.0072.776673148.593473
441.96TylerGarciaM408 Bradley RestDoe HillVA99Dance movement psychotherapist132537618638.674999-78.6324590113429700.0100.4408580FalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse12462.1241.9695.17809189.133972
594.63JenniferConnerF4655 David IslandDublinPA2158Transport planner132537624840.653382-76.1526670115960700.0101.4469050FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse57440.4194.6365.401685110.658809
644.54KelseyRichardsF889 Sarah Station Suite 624HolcombKS2691Arboriculturist132537628237.162705-100.1533700112753400.0100.4933000FalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse23784.3644.5490.289835129.427131
771.65StevenWilliamsM231 Flores Pass Suite 720EdinburgVA6018Designer, multimedia132537630838.948089-78.5402960117310700.0101.0439260FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse7666.5571.6568.635163113.994926
84.27HeatherChaseF6888 Hicks Stream Suite 954ManorPA1472Public affairs consultant132537631840.351813-79.9581460117925000.0100.0622520FalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse1067.504.2768.591883117.228359
9198.39MelissaAguilarF21326 Taylor Squares Suite 708ClarksvilleTN151785Pathologist132537636137.179198-87.4853810114656300.0102.1073541FalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse111693.57198.3994.141775133.033890
amtfirstlastgenderstreetcitystatecity_popjobunix_timemerch_latmerch_longis_frauddayday_of_weekagemerchant_encodedhourtime_diffday_of_monthis_weekendamt_ratiohigh_valuecategory_food_diningcategory_gas_transportcategory_grocery_netcategory_grocery_poscategory_health_fitnesscategory_homecategory_kids_petscategory_misc_netcategory_misc_poscategory_personal_carecategory_shopping_netcategory_shopping_poscategory_travelamt_merchant_interactionamt_day_interactionamt_meanamt_std
129666572.17JamesHuntM7369 Gabriel TunnelPointe Aux PinsMI95Electrical engineer137181652244.938461-83.9962340216262111210659.02110.7621420FalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse15227.87433.0294.69368189.230833
12966667.30AmberLewisF6296 John Keys Suite 858Pembroke TownshipIL2135Psychotherapist, child137181656240.556811-88.092339021616274126401.02110.1016910FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2000.2043.8071.78608672.979539
129666719.71ChristopherFarrellM97070 Anderson LandHaines CityFL33804Exercise physiologist137181665627.465871-81.5118040216292211213811.02110.2899410FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrue4355.91118.2667.979325210.676579
1296668100.85MargaretCurtisF742 Oneill ShoreFlorenceMS19685Fine artist137181668331.377697-90.528450021636424125451.02111.1307800FalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse42760.40605.1089.186195124.338770
129666937.38MarissaPowellF474 Allen HavenNorth LoupNE509Nurse, children's137181669641.728638-99.0396600216405981272413.02110.6142220FalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse22353.24224.2860.857433151.348005
129667015.56ErikPattersonM162 Jessica Row Apt. 072HatchUT258Geoscientist137181672836.841266-111.6907650216594991216781.02110.2462720FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse7764.4493.3663.18227498.227403
129667151.70JeffreyWhiteM8617 Holmes Terrace Suite 651TuscaroraMD100Production assistant, television137181673938.906881-78.2465280216412127962.02110.5111190TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse103.40310.20101.150621115.992546
1296672105.93ChristopherCastanedaM1632 Cohen Drive Suite 639High Rolls Mountain ParkNM899Naval architect137181675233.619513-105.1305290216535991229074.02111.6237970TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse63452.07635.5865.235995131.805092
129667374.90JosephMurrayM42933 Ryan UnderpassMandersonSD1126Volunteer coordinator137181681642.788940-103.2411600216405091291018.02110.7822150TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse38124.10449.4095.75369191.370450
12966744.30JeffreySmithM135 Joseph MountainsSulaMT218Therapist, horticultural137181681746.565983-114.1861100216253701244250.02110.0623830TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse1591.0025.8068.92919375.785422